The World Wide Web provides an extremely rich environment for information exploration and browsing. The dramatic speed of recent improvements in technology and usability has had a widespread effect, impacting nearly every aspect of the web experience. Innovations have resulted in improved accuracy and personalization of search technologies, see, for example, T. H. Haveliwala, “Topic-sensitive pagerank,” In Inter. WWW Conf., May 2002; R. Kraft et al., “Searching with context,” In Inter. WWW Conf., 2006; and U. Lee et al., “Automatic identification of user goals in web search,” In Inter. WWW Conf., 2005. Further, innovations have resulted in increasingly feature-packed web browsers, see, for example, A. Aula et al., “Information search and re-access strategies of experienced web users,” In Inter. WWW Conf., 2005; and A. Faaborg et al., “A goal-oriented web browser,” In ACM CHI, pp. 751-760, New York, N.Y., USA, 2006, ACM Press. Finally, innovations have resulted in a more interactive and dynamic web user experience, see, for example J. J. Garrett et al., “Ajax: A new approach to web applications,” www.adaptivepath.com/_publications/essays/archives/000385.php; and Google Reader, http://www.google.com/reader/view/.
These innovations allow users to perform certain online tasks more easily than ever before. For example, users can easily complete information seeking tasks at the click of a mouse. Store locations, ticket prices, and product information are now easily accessible using modern search engines and a default web browser.
Browsing tasks are also well supported by today's web tools. Web users can leaf through digital product catalogs, browse blogs and news content, or troll through massive video archives thanks to large webs of information built by content providers, or assembled via tagging and other social-based tools.
Additionally, users can purchase books, pay bills, or get driving directions directly from their web browser. These procedural tasks are made possible by web sites designed to support the specific procedural behaviors that are required to perform the task. For example, every user that buys items from Amazon.com will go through a standard “check-out” procedure to complete their transaction.
What have not received as much attention are sensemaking tasks. Sensemaking is a complex behavior where users gather and comprehend information from many sources over many different browsing sessions to answer potentially vague, non-procedural questions. Sensemaking tasks are common and include, for example, researching vacation destinations, deciding how to invest, or choosing where to buy a home. Many professional tasks fall into this category as well, such as open-source business intelligence, criminal and intelligence analysis, and investigative research.
Sensemaking tasks are not well supported with today's web tools. This is in part because they require cross-session, cross-site integration of information which by definition cannot be satisfied by any single content provider. The lack of support for sensemaking is why many people keep pencil and paper near their computer to take handwritten notes as they perform research online.
Sensemaking as a process has been studied for many years. In the early 1990s, researchers recognized the importance of understanding sensemaking behavior when designing knowledge representational tools, information retrieval systems, and user interfaces, see, for example, D. M. Russell et al., “The cost structure of sensemaking,” In ACM CHI, 1993. Closely related, information foraging theory, see, for example, P. Pirolli et al., “Information foraging,” Psychological Review, Vol. 106, pp. 643-675, 1999, was proposed to help understand how users adapt to technology when performing information seeking and consumption tasks.
More recently, this area of study has received additional attention. In particular, a renewed focus on sensemaking has developed within the intelligence analysis domain, see, for example, D. Gotz et al., “A study of information gathering and result processing in intelligence analysis,” In IUI 2006 Workshop on IUI for Intelligence Analysis, 2006, and P. Pirolli et al., “The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis,” In Inter. Conf. on Intelligence Analysis, 2005, and the emerging field of visual analytics, see, for example, J. J. Thomas et al., editors, “Illuminating the Path: The Research and Development Agenda for Visual Analytics,” IEEE Press, 2005.
Given the recent focus on sensemaking behavior, a number of dedicated tools have emerged that aim to assist users as they organize hypotheses, stage discovered information, and illustrate conclusions to collaborators. These include commercial tools for tasks such as brainstorming, such as, for example, Mindjet Corp., “Mind Manager,” www.mindjet.com, and criminal investigation management, such as, for example, i2 Incorporated, “Analyst's Notebook,” www.i2inc.com. Research efforts in visual analytics have led to additional contributions that more closely integrate information exploration and knowledge management tools, see, for example, D. Gotz et al., “Interactive visual synthesis of analytic knowledge,” In IEEE VAST, November 2006, and W. Wright et al., “The sandbox for analysis: concepts and evaluation,” In ACM CHI, pp. 801-810, New York, N.Y., USA, 2006, ACM Press.
The standard web browser provides a set of long-standing features to support basic re-access. These include bookmarks (or favorites), the back button, and page history. More recently, researchers have explored using visual tools for managing and exposing bookmarks as iconic thumbnails, see, for example, B. Amento et al., “Topicshop: enhanced support for evaluating and organizing collections of web sites,” In UIST, pp. 201-209, 2000. Other tools have extended this idea to include “link preview” features that show thumbnails of link targets as well, see, for example, N. Milic-Frayling et al., “Webscout: Support for revisitation of web pages within a navigation session,” In IEEE/WIC Inter. Conf. on Web Intelligence, 2003. Studies have shown that tools which organize bookmarks as thumbnails can improve user performance during certain tasks, see, for example, A. Aula et al., “Information search and re-access strategies of experienced web users,” In Inter. WWW Conf., 2005.
It has also been shown that cross-session capabilities are important because information tasks very often span more than just a single browsing session, see, for example, N. Jhaveri et al., “The advantages of a cross-session web workspace,” In CHI Extended Abstracts, pp. 1949-1952, New York, N.Y., USA, 2005, ACM Press. Cross-session re-access is also supported by the less-graphical Google Notebook tool, Google Notebook, http://www.google.com/notebook/, which adds the ability to share information and gain access to the work of others.
Exploiting user behavior on the web has become an important topic in recent years. The power of large crowds has been exploited in several ways, including social bookmarking, see, for example, T. Hammond et al., “Social bookmarking tools: A general review,” In Digital Library Magazine, Vol. 11, Nature Publishing Group, 2005 and tagging tools, see, for example, M. Dubinko et al., “Visualizing tags over time,” In Inter. WWW Conf., 2006. However, while global knowledge across user populations can certainly be exploited during sensemaking tasks, a critical component is how to take advantage of an individual's unique progress on a specific task, not the consensus behavior of a crowd.
Personalized search is perhaps the most active area of research into personalization, see, for example, G. Rossi et al., “Designing personalized web applications,” In Inter. WWW Conf., 2001. These tools incorporate implicit [G. Jeh et al., “Scaling personalized web search,” In Inter. WWW Conf., 2003; R. Kraft et al., “Searching with context,” In Inter. WWW Conf., 2006; U. Lee et al., “Automatic identification of user goals in web search,” In Inter. WWW Conf., 2005; and K. Sugiyama et al., “Adaptive web search based on user profile constructed without any effort from users,” In Inter. WWW Conf., 2004] or explicit [P. Ferragina et al., “A personalized search engine based on web-snippet hierarchical clustering,” In Inter. WWW Conf., 2005; and Z. Wen et al., “Context-aware, adaptive information retrieval for investigative tasks,” In To Appear in Inter. Conf on IUI, 2007] user behavior to improve the set of results returned from a search. Personalized search techniques can be extremely valuable in sensemaking. Other research has examined cross-site personalization for procedural tasks using script-by-example techniques; see, for example, A. Faaborg et al., “A goal-oriented web browser,” In ACM CHI, pp. 751-760, New York, N.Y., USA, 2006, ACM Press.
Relocation is a common event that families must face after a change in employment. Moving to a new and unknown city can be a stressful and complex task that requires careful research and attention to detail. Before choosing where to live in a new place, it is important to learn as much as possible about the area.
Like many tasks, there are several standard questions that are broadly applicable: Where are the best public schools? Which towns or cities are unsafe? Are there high traffic areas that can impact my commute? What are typical home prices in the area? These are questions that a real estate portal would try to help answer.
In addition, however, there are likely to be questions that address needs that are unique to a particular person. Which neighborhoods have my kind of religious institution? Which communities have a sizable population of my ethnic group? Are there adult soccer leagues that I can join? Which hospitals have specialists that can treat my child's rare disease?
Answering all of these questions in a comprehensive way is a long term, non-procedural task that requires reasoning over information gathered from many different sources. For example, simply finding out which communities have sizable ethnic populations can require web searching across several sites. Comparing the locations of those communities with possible work locations and commute times requires even more research and information correlation.
Harder still is the discovery of serendipitous connections that a user might not be explicitly trying to find. For example, discovering that one particular street has not only a religious institution, but also soccer fields and a large hospital can be very difficult, particularly if the user's search for each set of information occurs days apart.
The relocation scenario described above is a prime example of how current web tools are invaluable for finding relevant information, yet insufficient for completing a sensemaking task. Search engines and content providers make all sorts of information accessible and discoverable, but there are few tools for gathering and organizing information from multiple sources into a single workspace to facilitate insight and improve the chances for serendipitous discovery.
A web-based tool designed to support this type of sensemaking scenario would be most effective if it met the following requirements:
Site Independence: A sensemaking tool should be independent of any particular site. As the example above indicates, no single content provider can host every piece of information relevant to a sufficiently complex task. Therefore, any tool designed to support sensemaking should work across all web sites accessed by the user.
Capture of discovered information: Capturing information, such as hospital locations and school rankings, can allow them to be reviewed later in time as additional information is discovered. This is especially important for long-term tasks where recall can be difficult.
Capture of insight: Users create new knowledge during sensemaking tasks. For example, the user in the scenario discovered a street with several strong qualities, including a hospital, soccer fields, and a religious facility. Tools for recording this discovery, which doesn't exist on any single web page, allow users to organize captured information in the context of their own thoughts.
Assistance in connection discovery: The long-term and multi-topic properties of sensemaking tasks pose a challenge to users that must discover connections between what has already been discovered and what is currently being explored in their browser. In the scenario, for example, it could be difficult for the user to recall that the street with the best hospital is the same street with soccer fields if the user's searches for each type of information were performed a week apart. A sensemaking support tool should help increase the odds that this sort of connection is discovered.
A web-based environment that meets the above requirements, coupled with existing web technologies for information seeking and browsing, would be a powerful tool to support complex sensemaking tasks.